Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns

This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-dimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators

An Effective Clustering Approach to Stock Market Prediction

In this paper, we propose an effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering), to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the converted feature vectors into clusters. Second, for each cluster, we recursively apply the K-means clustering method to partition each cluster into sub-clusters so that most feature vectors in each sub-cluster belong to the same class. Then, for each sub-cluster, we choose its centroid as the representative feature vector. Finally, we employ the representative feature vectors to predict the stock price movements. The experimental results show the proposed method outperforms SVM in terms of accuracy and average profits

Clarifying the Dominant Logic Construct by Disentangling and Reassembling its Dimensions

Since its introduction, Prahalad and Bettis's concept of dominant logic has informed a variety of scholarly conversations in management and strategy research. However, scholars have interpreted dominant logic in different ways, emphasizing different aspects, such as managerial mindsets, administrative tools and management functions, as defining elements. Similarly, empirical studies have captured various aspects, such as meanings of entrepreneurs, observable strategic decisions and business model similarity, as indicators of dominant logic. Consequently, the concept lacks analytical clarity, and it is difficult to compare or generalize findings from this diverse set of studies. The aim of this review is to improve conceptual clarity by analysing, comparing and evaluating the existing interpretations and assessments of dominant logic in 94 studies. In the first part of the review, by disentangling the interpretations of the concept, we show that dominant logic consists of four defining dimensions: (i) shared mental models; (ii) values and premises; (iii) organizational practices; and (iv) organizing structures. In the second part, we reassemble dominant logic into an integrative model and theorize about how these dimensions operate in concert to produce a firm's dominant logic. Thus, our main contribution is a clarification and synthesis of the literature, which comes with implications on how future research can conceptualize and operationalize dominant logic more consistently

Towards improving WEBSOM with multi-word expressions

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaLarge quantities of free-text documents are usually rich in information and covers several topics. However, since their dimension is very large, searching and filtering data is an exhaustive task. A large text collection covers a set of topics where each topic is affiliated to a group of documents. This thesis presents a method for building a document map about the core contents covered in the collection. WEBSOM is an approach that combines document encoding methods and Self-Organising Maps (SOM) to generate a document map. However, this methodology has a weakness in the document encoding method because it uses single words to characterise documents. Single words tend to be ambiguous and semantically vague, so some documents can be incorrectly related. This thesis proposes a new document encoding method to improve the WEBSOM approach by using multi word expressions (MWEs) to describe documents. Previous research and ongoing experiments encourage us to use MWEs to characterise documents because these are semantically more accurate than single words and more descriptive